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The Artificial Intelligence industry regularly develops applications thatmostly rely on Knowledge Bases, a data repository about specific, or general,domains, usually represented in a graph shape. Similar to other databases, theyface two main challenges: information ingestion and information retrieval. Weapproach these challenges by jointly learning graph extraction from text andtext generation from graphs. The proposed solution, a T5 architecture, istrained in a multi-task semi-supervised environment, with our collectednon-parallel data, following a cycle training regime. Experiments on WebNLGdataset show that our approach surpasses unsupervised state-of-the-art resultsin text-to-graph and graph-to-text. More relevantly, our framework is moreconsistent across seen and unseen domains than supervised models. The resultingmodel can be easily trained in any new domain with non-parallel data, by simplyadding text and graphs about it, in our cycle framework.

A multi-task semi-supervised framework for Text2Graph & Graph2Text